The present disclosure relates, generally, to systems and method for processing optical images. More particularly, the disclosure relates to automatic detection of polyps in an optical images.
Colorectal cancer is the second highest cause of cancer-related deaths in the United States with 51,690 estimated deaths in 2012. Colorectal cancer most often begins in the form of small polyps-abnormal growth of the colon surface. The preferred screening method for polyp detection and removal is optical colonoscopy (OC), during which colonoscopist meticulously examine the colon wall to find and remove polyps. Despite many screening and therapeutic advantages, polyp detection with OC remains a challenging task and as evidenced by a recent clinical study, wherein 22% of polyps remained undetected during colon screening with OC. Similar polyp miss rates have also been reported by other clinical studies. To compound the problem, between 4% to 6% of the colorectal cancers diagnosed are thought to be missed on prior colonoscopy. It is therefore important to reduce polyp miss rate as it decreases the incidence and mortality of colorectal cancer.
Computer-aided polyp detection has recently been considered as a tool for reducing polyp miss-rate, where the idea is to highlight regions with suspected polyps during a colonoscopy procedure. Existing algorithms for automatic polyp detection have, thus far, primarily relied upon texture or shape information for detecting polyps. Although texture is a distinguishing characteristic of polyps, merely relying on texture may not address the automatic detection problem. For example, the texture of a polyp becomes fully visible only if the camera captures close shots of the surface of a polyp. This condition is often met when polyps have already been detected by operators, which obviously eliminates the need for computer-aided detection. On the other hand, shape information cannot be considered as a reliable measure since polyps appear in a variety of forms ranging from sessile to peduncular shapes.
Consequently, considering such limitations of previous technological approaches, it would be desirable to have a system and method for accurate and reliable polyp detection in optical colonoscopy images that is shape-independent and mainly captures color variation across the boundary of polyps.